First assessment of Goidanich Index and aerobiological data for Plasmopara viticola infection risk management in north-west Spain

• Published in: The Journal of agricultural science 2019-03, Vol.157 (2), p.129-139
• Main Authors: Fernández-González, M., Piña-Rey, A., González-Fernández, E., Aira, M. J., Rodríguez-Rajo, F. J.
• Format: Article
• Language: English
• Subjects: Airborne microorganisms; Algorithms; Climate; Crops and Soils Research Paper; Developmental stages; Disease; Downy mildew; Environmental conditions; Fruits; Fungal diseases; Fungi; Fungicides; Grapevines; Health risks; High temperature; Humidity; Infections; Leaves; Pathogens; Plasmopara viticola; Rainfall; Relative humidity; Ripening; Risk management; Secondary infection; Sensors; Vineyards; Wineries & vineyards
• ISSN: 0021-8596; 1469-5146
• DOI: 10.1017/S0021859619000376

The climate of north-western Spain, with high temperatures and relative humidity during the grapevine vegetative cycle, can especially favour the development of fungal diseases in vineyards. One of the most important diseases is downy mildew, caused by the fungus Plasmopara viticola. The aim of the current study is to propose a system containing phenological data, biological sensors of pathogen indicator and the agrometeorological Goidanich Index in order to optimize the application of downy mildew fungicide treatments. The study was conducted in a vineyard of the ‘Ribeiro’ Designation of Origin region from 2005 to 2016 during the Vitis vegetative period. Aerobiological sampling was performed using a LANZONI VPPS-2000 volumetric trap. The highest number of infection cycles was recorded during the 2009 harvest, with a total of 16 cycles. Years with fewer infection cycles were 2008, 2011 and 2012 (13 cycles). Primary infections were produced during the third fortnight of April and a high amount of secondary infection cycles were detected by the Goidanich algorithm during the fruit development and berry ripening stages. The best estimators of the P. viticola spore concentrations were the fungus spore levels during the previous day, the average temperature 5 days before and rainfall 2 days before. The regression equation obtained accounted for the 95.9% of the spore concentration variation. The combination of the Goidanich index and biological sensors provides a valuable tool to establish an accurate, modern, integrated downy mildew pest-management strategy.